pdmlabs.experiment.batch.SA_experiment

pdmlabs.experiment.batch.SA_experiment#

Classes

Supervised_SA_PdMExperiment(experiment_name, ...)

Supervised Survival Analysis (SA) experiment.
class pdmlabs.experiment.batch.SA_experiment.Supervised_SA_PdMExperiment(experiment_name: str, pipeline: PdMPipeline, param_space: dict, constraint_function: Callable = None, target_data: list[DataFrame] = None, target_sources: list[str] = None, historic_data: list[DataFrame] = [], historic_sources: list[str] = [], optimization_param: str = 'AD1_AUC', initial_random: int = 2, num_iteration: int = 20, batch_size: int = 1, n_jobs: int = 1, random_state: int = 42, random_n_tries: int = 3, constraint_max_retries: int = 10, historic_data_header: str = 'infer', target_data_header: str = 'infer', artifacts: str = 'artifacts', debug: bool = False, delay: float = None, log_best_scores: bool = False, maximize: bool = True, custom_evaluators: list = None)#

Bases: PdMExperiment

Supervised Survival Analysis (SA) experiment.

This experiment flavor combines supervised learning with survival analysis concepts: - Treats PdM as a survival prediction problem - Uses labeled data to train models that predict survival curves - Evaluates using survival analysis metrics (Kaplan-Meier, concordance, etc.)

Useful for: - Complex failure time modeling - Understanding failure hazards and risks - Competing risks scenarios

Raises:

IncompatibleMethodException – If method does not implement SupervisedMethodInterface.

Examples

>>> experiment = Supervised_SA_PdMExperiment(
...     experiment_name='survival-demo',
...     pipeline=pipeline,
...     param_space={'method_alpha': [0.01, 0.1, 1.0]},
...     num_iteration=20
... )
>>> results = experiment.execute()
execute() → dict#

Run supervised survival analysis experiment.

Applies survival analysis techniques to PdM: 1. Fits method on labeled historic data 2. For each target scenario:

  1. Fits model to scenario-specific data

  2. Generates survival predictions

  3. Evaluates against ground truth

  1. Returns best parameters

Returns:

Result dictionary with best_params, best_objective, and SA metrics.

Return type:

dict

Raises:

IncompatibleMethodException – If method is not SupervisedMethodInterface.

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